
from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    torch.FloatTensor




class 							_UpperCamelCase					(       nn.Module	):






			'''simple docstring'''

			def __init__(							self			:							List[Any]    ,						snake_case_			:							str=3    ,						snake_case_			:							str=3    ,						snake_case_			:							Any=("DownEncoderBlock2D",)    ,						snake_case_			:							Optional[Any]=(64,)    ,						snake_case_			:							Dict=2    ,						snake_case_			:							Optional[int]=32    ,						snake_case_			:							Dict="silu"    ,						snake_case_			:							List[str]=True    ,						):
							super().__init__()
							UpperCamelCase_:				int						=    layers_per_block

							UpperCamelCase_:				Any						=    torch.nn.Convad(
							    snake_case_    ,						block_out_channels[0]    ,						kernel_size=3    ,						stride=1    ,						padding=1    ,						)

							UpperCamelCase_:				Optional[Any]						=    None
							UpperCamelCase_:				Optional[int]						=    nn.ModuleList([]   )

							# down
							UpperCamelCase_:				Optional[Any]						=    block_out_channels[0]
							for i, down_block_type in enumerate(snake_case_   ):
											UpperCamelCase_:				Optional[Any]						=    output_channel
											UpperCamelCase_:				List[str]						=    block_out_channels[i]
											UpperCamelCase_:				List[Any]						=    i == len(snake_case_   ) - 1

											UpperCamelCase_:				int						=    get_down_block(
											    snake_case_    ,						num_layers=self.layers_per_block    ,						in_channels=snake_case_    ,						out_channels=snake_case_    ,						add_downsample=not is_final_block    ,						resnet_eps=1e-6    ,						downsample_padding=0    ,						resnet_act_fn=snake_case_    ,						resnet_groups=snake_case_    ,						attention_head_dim=snake_case_    ,						temb_channels=snake_case_    ,						)
											self.down_blocks.append(snake_case_   )

							# mid
							UpperCamelCase_:				Dict						=    UNetMidBlockaD(
							    in_channels=block_out_channels[-1]    ,						resnet_eps=1e-6    ,						resnet_act_fn=snake_case_    ,						output_scale_factor=1    ,						resnet_time_scale_shift="""default"""    ,						attention_head_dim=block_out_channels[-1]    ,						resnet_groups=snake_case_    ,						temb_channels=snake_case_    ,						)

							# out
							UpperCamelCase_:				Optional[int]						=    nn.GroupNorm(num_channels=block_out_channels[-1]    ,						num_groups=snake_case_    ,						eps=1e-6   )
							UpperCamelCase_:				int						=    nn.SiLU()

							UpperCamelCase_:				int						=    2 * out_channels if double_z else out_channels
							UpperCamelCase_:				Tuple						=    nn.Convad(block_out_channels[-1]    ,						snake_case_    ,						3    ,						padding=1   )

							UpperCamelCase_:				Tuple						=    False




			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Tuple   ):
							UpperCamelCase_:				int						=    x
							UpperCamelCase_:				Optional[Any]						=    self.conv_in(snake_case_   )

							if self.training and self.gradient_checkpointing:

											def create_custom_forward(snake_case_			:							Any   ):
															def custom_forward(*snake_case_			:							int   ):
																			return module(*snake_case_   )

															return custom_forward

											# down
											if is_torch_version(""">="""    ,						"""1.11.0"""   ):
															for down_block in self.down_blocks:
																			UpperCamelCase_:				Union[str, Any]						=    torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(snake_case_   )    ,						snake_case_    ,						use_reentrant=snake_case_   )
															# middle
															UpperCamelCase_:				Optional[Any]						=    torch.utils.checkpoint.checkpoint(
															    create_custom_forward(self.mid_block   )    ,						snake_case_    ,						use_reentrant=snake_case_   )
											else:
															for down_block in self.down_blocks:
																			UpperCamelCase_:				Optional[int]						=    torch.utils.checkpoint.checkpoint(create_custom_forward(snake_case_   )    ,						snake_case_   )
															# middle
															UpperCamelCase_:				List[str]						=    torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block   )    ,						snake_case_   )

							else:
											# down
											for down_block in self.down_blocks:
															UpperCamelCase_:				List[str]						=    down_block(snake_case_   )

											# middle
											UpperCamelCase_:				int						=    self.mid_block(snake_case_   )

							# post-process
							UpperCamelCase_:				Dict						=    self.conv_norm_out(snake_case_   )
							UpperCamelCase_:				List[str]						=    self.conv_act(snake_case_   )
							UpperCamelCase_:				List[Any]						=    self.conv_out(snake_case_   )

							return sample




class 							_UpperCamelCase					(       nn.Module	):






			'''simple docstring'''

			def __init__(							self			:							Any    ,						snake_case_			:							Dict=3    ,						snake_case_			:							Any=3    ,						snake_case_			:							Any=("UpDecoderBlock2D",)    ,						snake_case_			:							Dict=(64,)    ,						snake_case_			:							Optional[int]=2    ,						snake_case_			:							int=32    ,						snake_case_			:							List[Any]="silu"    ,						snake_case_			:							List[str]="group"    ,						):
							super().__init__()
							UpperCamelCase_:				Dict						=    layers_per_block

							UpperCamelCase_:				int						=    nn.Convad(
							    snake_case_    ,						block_out_channels[-1]    ,						kernel_size=3    ,						stride=1    ,						padding=1    ,						)

							UpperCamelCase_:				Union[str, Any]						=    None
							UpperCamelCase_:				str						=    nn.ModuleList([]   )

							UpperCamelCase_:				List[str]						=    in_channels if norm_type == """spatial""" else None

							# mid
							UpperCamelCase_:				Optional[Any]						=    UNetMidBlockaD(
							    in_channels=block_out_channels[-1]    ,						resnet_eps=1e-6    ,						resnet_act_fn=snake_case_    ,						output_scale_factor=1    ,						resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type    ,						attention_head_dim=block_out_channels[-1]    ,						resnet_groups=snake_case_    ,						temb_channels=snake_case_    ,						)

							# up
							UpperCamelCase_:				List[Any]						=    list(reversed(snake_case_   )   )
							UpperCamelCase_:				List[str]						=    reversed_block_out_channels[0]
							for i, up_block_type in enumerate(snake_case_   ):
											UpperCamelCase_:				Union[str, Any]						=    output_channel
											UpperCamelCase_:				Tuple						=    reversed_block_out_channels[i]

											UpperCamelCase_:				Any						=    i == len(snake_case_   ) - 1

											UpperCamelCase_:				str						=    get_up_block(
											    snake_case_    ,						num_layers=self.layers_per_block + 1    ,						in_channels=snake_case_    ,						out_channels=snake_case_    ,						prev_output_channel=snake_case_    ,						add_upsample=not is_final_block    ,						resnet_eps=1e-6    ,						resnet_act_fn=snake_case_    ,						resnet_groups=snake_case_    ,						attention_head_dim=snake_case_    ,						temb_channels=snake_case_    ,						resnet_time_scale_shift=snake_case_    ,						)
											self.up_blocks.append(snake_case_   )
											UpperCamelCase_:				List[str]						=    output_channel

							# out
							if norm_type == "spatial":
											UpperCamelCase_:				Optional[Any]						=    SpatialNorm(block_out_channels[0]    ,						snake_case_   )
							else:
											UpperCamelCase_:				Dict						=    nn.GroupNorm(num_channels=block_out_channels[0]    ,						num_groups=snake_case_    ,						eps=1e-6   )
							UpperCamelCase_:				Union[str, Any]						=    nn.SiLU()
							UpperCamelCase_:				Union[str, Any]						=    nn.Convad(block_out_channels[0]    ,						snake_case_    ,						3    ,						padding=1   )

							UpperCamelCase_:				Optional[int]						=    False




			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							str=None   ):
							UpperCamelCase_:				Dict						=    z
							UpperCamelCase_:				List[Any]						=    self.conv_in(snake_case_   )

							UpperCamelCase_:				Dict						=    next(iter(self.up_blocks.parameters()   )   ).dtype
							if self.training and self.gradient_checkpointing:

											def create_custom_forward(snake_case_			:							int   ):
															def custom_forward(*snake_case_			:							Dict   ):
																			return module(*snake_case_   )

															return custom_forward

											if is_torch_version(""">="""    ,						"""1.11.0"""   ):
															# middle
															UpperCamelCase_:				Optional[Any]						=    torch.utils.checkpoint.checkpoint(
															    create_custom_forward(self.mid_block   )    ,						snake_case_    ,						snake_case_    ,						use_reentrant=snake_case_   )
															UpperCamelCase_:				Any						=    sample.to(snake_case_   )

															# up
															for up_block in self.up_blocks:
																			UpperCamelCase_:				Optional[int]						=    torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(snake_case_   )    ,						snake_case_    ,						snake_case_    ,						use_reentrant=snake_case_   )
											else:
															# middle
															UpperCamelCase_:				Optional[Any]						=    torch.utils.checkpoint.checkpoint(
															    create_custom_forward(self.mid_block   )    ,						snake_case_    ,						snake_case_   )
															UpperCamelCase_:				Optional[int]						=    sample.to(snake_case_   )

															# up
															for up_block in self.up_blocks:
																			UpperCamelCase_:				Tuple						=    torch.utils.checkpoint.checkpoint(create_custom_forward(snake_case_   )    ,						snake_case_    ,						snake_case_   )
							else:
											# middle
											UpperCamelCase_:				List[Any]						=    self.mid_block(snake_case_    ,						snake_case_   )
											UpperCamelCase_:				Tuple						=    sample.to(snake_case_   )

											# up
											for up_block in self.up_blocks:
															UpperCamelCase_:				Optional[Any]						=    up_block(snake_case_    ,						snake_case_   )

        # post-process
							if latent_embeds is None:
											UpperCamelCase_:				Union[str, Any]						=    self.conv_norm_out(snake_case_   )
							else:
											UpperCamelCase_:				str						=    self.conv_norm_out(snake_case_    ,						snake_case_   )
							UpperCamelCase_:				List[Any]						=    self.conv_act(snake_case_   )
							UpperCamelCase_:				Union[str, Any]						=    self.conv_out(snake_case_   )

							return sample




class 							_UpperCamelCase					(       nn.Module	):






			'''simple docstring'''

			def __init__(							self			:							Optional[int]    ,						snake_case_			:							str    ,						snake_case_			:							int    ,						snake_case_			:							Any    ,						snake_case_			:							Any=None    ,						snake_case_			:							Dict="random"    ,						snake_case_			:							List[Any]=False    ,						snake_case_			:							str=True   ):
							super().__init__()
							UpperCamelCase_:				List[Any]						=    n_e
							UpperCamelCase_:				Optional[int]						=    vq_embed_dim
							UpperCamelCase_:				Tuple						=    beta
							UpperCamelCase_:				Dict						=    legacy

							UpperCamelCase_:				Optional[Any]						=    nn.Embedding(self.n_e    ,						self.vq_embed_dim   )
							self.embedding.weight.data.uniform_(-1.0 / self.n_e    ,						1.0 / self.n_e   )

							UpperCamelCase_:				Any						=    remap
							if self.remap is not None:
											self.register_buffer("""used"""    ,						torch.tensor(np.load(self.remap   )   )   )
											UpperCamelCase_:				str						=    self.used.shape[0]
											UpperCamelCase_:				List[Any]						=    unknown_index  # "random" or "extra" or integer
											if self.unknown_index == "extra":
															UpperCamelCase_:				Union[str, Any]						=    self.re_embed
															UpperCamelCase_:				Optional[int]						=    self.re_embed + 1
											print(
											    f'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
											    f'''Using {self.unknown_index} for unknown indices.'''   )
							else:
											UpperCamelCase_:				Optional[int]						=    n_e

							UpperCamelCase_:				Tuple						=    sane_index_shape

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							Optional[Any]   ):
							UpperCamelCase_:				List[str]						=    inds.shape
							assert len(snake_case_   ) > 1
							UpperCamelCase_:				Dict						=    inds.reshape(ishape[0]    ,						-1   )
							UpperCamelCase_:				str						=    self.used.to(snake_case_   )
							UpperCamelCase_:				List[Any]						=    (inds[:, :, None] == used[None, None, ...]).long()
							UpperCamelCase_:				Any						=    match.argmax(-1   )
							UpperCamelCase_:				str						=    match.sum(2   ) < 1
							if self.unknown_index == "random":
											UpperCamelCase_:				str						=    torch.randint(0    ,						self.re_embed    ,						size=new[unknown].shape   ).to(device=new.device   )
							else:
											UpperCamelCase_:				Optional[int]						=    self.unknown_index
							return new.reshape(snake_case_   )

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Tuple   ):
							UpperCamelCase_:				Union[str, Any]						=    inds.shape
							assert len(snake_case_   ) > 1
							UpperCamelCase_:				Tuple						=    inds.reshape(ishape[0]    ,						-1   )
							UpperCamelCase_:				int						=    self.used.to(snake_case_   )
							if self.re_embed > self.used.shape[0]:  # extra token
											UpperCamelCase_:				Optional[int]						=    0  # simply set to zero
							UpperCamelCase_:				Optional[Any]						=    torch.gather(used[None, :][inds.shape[0] * [0], :]    ,						1    ,						snake_case_   )
							return back.reshape(snake_case_   )

			def    lowerCAmelCase__						(							self			:							Tuple    ,						snake_case_			:							Tuple   ):
							# reshape z -> (batch, height, width, channel) and flatten
							UpperCamelCase_:				int						=    z.permute(0    ,						2    ,						3    ,						1   ).contiguous()
							UpperCamelCase_:				Dict						=    z.view(-1    ,						self.vq_embed_dim   )

							# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
							UpperCamelCase_:				Union[str, Any]						=    torch.argmin(torch.cdist(snake_case_    ,						self.embedding.weight   )    ,						dim=1   )

							UpperCamelCase_:				Union[str, Any]						=    self.embedding(snake_case_   ).view(z.shape   )
							UpperCamelCase_:				List[Any]						=    None
							UpperCamelCase_:				List[Any]						=    None

							# compute loss for embedding
							if not self.legacy:
											UpperCamelCase_:				int						=    self.beta * torch.mean((z_q.detach() - z) ** 2   ) + torch.mean((z_q - z.detach()) ** 2   )
							else:
											UpperCamelCase_:				int						=    torch.mean((z_q.detach() - z) ** 2   ) + self.beta * torch.mean((z_q - z.detach()) ** 2   )

							# preserve gradients
							UpperCamelCase_:				str						=    z + (z_q - z).detach()

							# reshape back to match original input shape
							UpperCamelCase_:				Dict						=    z_q.permute(0    ,						3    ,						1    ,						2   ).contiguous()

							if self.remap is not None:
											UpperCamelCase_:				Any						=    min_encoding_indices.reshape(z.shape[0]    ,						-1   )  # add batch axis
											UpperCamelCase_:				int						=    self.remap_to_used(snake_case_   )
											UpperCamelCase_:				Dict						=    min_encoding_indices.reshape(-1    ,						1   )  # flatten

							if self.sane_index_shape:
											UpperCamelCase_:				Optional[Any]						=    min_encoding_indices.reshape(z_q.shape[0]    ,						z_q.shape[2]    ,						z_q.shape[3]   )

							return z_q, loss, (perplexity, min_encodings, min_encoding_indices)




			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							Tuple    ,						snake_case_			:							Tuple   ):
							# shape specifying (batch, height, width, channel)
							if self.remap is not None:
											UpperCamelCase_:				Any						=    indices.reshape(shape[0]    ,						-1   )  # add batch axis
											UpperCamelCase_:				Optional[int]						=    self.unmap_to_all(snake_case_   )
											UpperCamelCase_:				Tuple						=    indices.reshape(-1   )  # flatten again

							# get quantized latent vectors
							UpperCamelCase_:				int						=    self.embedding(snake_case_   )

							if shape is not None:
											UpperCamelCase_:				Optional[int]						=    z_q.view(snake_case_   )
											# reshape back to match original input shape
											UpperCamelCase_:				Tuple						=    z_q.permute(0    ,						3    ,						1    ,						2   ).contiguous()

							return z_q




class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			def __init__(							self			:							Optional[int]    ,						snake_case_			:							Dict    ,						snake_case_			:							List[str]=False   ):
							UpperCamelCase_:				Any						=    parameters
							UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    torch.chunk(snake_case_    ,						2    ,						dim=1   )
							UpperCamelCase_:				List[str]						=    torch.clamp(self.logvar    ,						-30.0    ,						20.0   )
							UpperCamelCase_:				int						=    deterministic
							UpperCamelCase_:				Union[str, Any]						=    torch.exp(0.5 * self.logvar   )
							UpperCamelCase_:				List[str]						=    torch.exp(self.logvar   )
							if self.deterministic:
											UpperCamelCase_:				str						=    torch.zeros_like(
											    self.mean    ,						device=self.parameters.device    ,						dtype=self.parameters.dtype   )

			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							Optional[torch.Generator] = None   ):
							# make sure sample is on the same device as the parameters and has same dtype
							UpperCamelCase_:				Optional[Any]						=    randn_tensor(
							    self.mean.shape    ,						generator=snake_case_    ,						device=self.parameters.device    ,						dtype=self.parameters.dtype   )
							UpperCamelCase_:				str						=    self.mean + self.std * sample
							return x

			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							Dict=None   ):
							if self.deterministic:
											return torch.Tensor([0.0]   )
							else:
											if other is None:
															return 0.5 * torch.sum(torch.pow(self.mean    ,						2   ) + self.var - 1.0 - self.logvar    ,						dim=[1, 2, 3]   )
											else:
															return 0.5 * torch.sum(
															    torch.pow(self.mean - other.mean    ,						2   ) / other.var
															    + self.var / other.var
															    - 1.0
															    - self.logvar
															    + other.logvar    ,						dim=[1, 2, 3]    ,						)

			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							Any    ,						snake_case_			:							Any=[1, 2, 3]   ):
							if self.deterministic:
											return torch.Tensor([0.0]   )
							UpperCamelCase_:				int						=    np.log(2.0 * np.pi   )
							return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean    ,						2   ) / self.var    ,						dim=snake_case_   )




			def    lowerCAmelCase__						(							self			:							Any   ):
							return self.mean








import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment







def 							A__       (    lowerCamelCase  ,     lowerCamelCase=()  ,     lowerCamelCase=None  ,     lowerCamelCase="no"  ,     lowerCamelCase="29500"    )	->						List[Any]:
				UpperCamelCase_:				Any						=    False
				UpperCamelCase_:				int						=    False
				if any(key.startswith("""KAGGLE"""    ) for key in os.environ.keys()    ):
								UpperCamelCase_:				str						=    True
				elif "IPython" in sys.modules:
								UpperCamelCase_:				Tuple						=    """google.colab""" in str(sys.modules["""IPython"""].get_ipython()    )

				try:
								UpperCamelCase_:				Union[str, Any]						=    PrecisionType(mixed_precision.lower()    )
				except ValueError:
								raise ValueError(
								    F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''    )

				if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME"""  ,     lowerCamelCase    ) is not None):
								# TPU launch
								import torch_xla.distributed.xla_multiprocessing as xmp

								if len(AcceleratorState._shared_state    ) > 0:
												raise ValueError(
												    """To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
												    """your training function. Restart your notebook and make sure no cells initializes an """
												    """`Accelerator`."""    )
								if num_processes is None:
												UpperCamelCase_:				str						=    8

								UpperCamelCase_:				int						=    PrepareForLaunch(lowerCamelCase  ,     distributed_type="""TPU"""    )
								print(F'''Launching a training on {num_processes} TPU cores.'''    )
								xmp.spawn(lowerCamelCase  ,     args=lowerCamelCase  ,     nprocs=lowerCamelCase  ,     start_method="""fork"""    )
				elif in_colab:
								# No need for a distributed launch otherwise as it's either CPU or one GPU.
								if torch.cuda.is_available():
												print("""Launching training on one GPU."""    )
								else:
												print("""Launching training on one CPU."""    )
								function(*lowerCamelCase    )
				else:
								if num_processes is None:
												raise ValueError(
												    """You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."""    )

								if num_processes > 1:
												# Multi-GPU launch
												from torch.multiprocessing import start_processes
												from torch.multiprocessing.spawn import ProcessRaisedException

												if len(AcceleratorState._shared_state    ) > 0:
																raise ValueError(
																    """To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
																    """inside your training function. Restart your notebook and make sure no cells initializes an """
																    """`Accelerator`."""    )

												if torch.cuda.is_initialized():
																raise ValueError(
																    """To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
																    """using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
																    """function."""    )

												# torch.distributed will expect a few environment variable to be here. We set the ones common to each
												# process here (the other ones will be set be the launcher).
												with patch_environment(
												    world_size=lowerCamelCase  ,     master_addr="""127.0.01"""  ,     master_port=lowerCamelCase  ,     mixed_precision=lowerCamelCase    ):
																UpperCamelCase_:				Optional[int]						=    PrepareForLaunch(lowerCamelCase  ,     distributed_type="""MULTI_GPU"""    )
																print(F'''Launching training on {num_processes} GPUs.'''    )
																try:
																				start_processes(lowerCamelCase  ,     args=lowerCamelCase  ,     nprocs=lowerCamelCase  ,     start_method="""fork"""    )
																except ProcessRaisedException as e:
																				if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																								raise RuntimeError(
																								    """CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
																								    """This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
																								    """Please review your imports and test them when running the `notebook_launcher()` to identify """
																								    """which one is problematic."""    ) from e

								else:
												# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
												if is_mps_available():
																UpperCamelCase_:				List[Any]						=    """1"""
																print("""Launching training on MPS."""    )
												elif torch.cuda.is_available():
																print("""Launching training on one GPU."""    )
												else:
																print("""Launching training on CPU."""    )
												function(*lowerCamelCase    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase=()  ,     lowerCamelCase=2    )	->						List[str]:
				from torch.multiprocessing import start_processes

				with tempfile.NamedTemporaryFile() as tmp_file:
								# torch.distributed will expect a few environment variable to be here. We set the ones common to each
								# process here (the other ones will be set be the launcher).
								with patch_environment(
								    world_size=lowerCamelCase  ,     master_addr="""127.0.01"""  ,     master_port="""29500"""  ,     accelerate_mixed_precision="""no"""  ,     accelerate_debug_rdv_file=tmp_file.name  ,     accelerate_use_cpu="""yes"""  ,     ):
												UpperCamelCase_:				str						=    PrepareForLaunch(lowerCamelCase  ,     debug=lowerCamelCase    )
												start_processes(lowerCamelCase  ,     args=lowerCamelCase  ,     nprocs=lowerCamelCase  ,     start_method="""fork"""    )






